Analysts, firms, and other organizations increasingly use analytical processing systems to perform complex analyses on data related to products, services, trading, and other items. With the increased reliance on complex data analytics, analytical processing systems have added sophisticated methods of segmenting and visualizing datasets. For example, some existing analytical processing systems apply sophisticated algorithms to analyze large datasets in less than seconds. In some cases, for instance, the algorithms enable the analytical processing systems to identify search terms that web visitors entered before visiting and purchasing a product from a particular website. As another example, in other cases, existing analytical processing systems identify different channels from either mobile software applications or websites from which a visitor navigated to a target website. Having performed these or other analytics operations, some existing analytical processing systems provide visualizations of the segmented datasets in various area charts, bar charts, timelines, or other graphical representations.
To enable the growing number and complexity of analytics operations, some analytical processing systems have modified user interfaces to include more options. For example, many existing analytics user interfaces include an increasing number of menu options, icons, search fields, or drag-and-drop tools that capture user inputs for analytics operations. Despite the growing flexibility of some options, several existing analytics user interfaces still require an analyst to use specific computational syntax to perform an analytics operation. For instance, in some cases, an analytics user interface can capture the necessary inputs for an analytics operation only when the analyst uses corresponding Structured Query Language (“SQL”) syntax.
Many analytics user interfaces have become too complex for some analysts to properly use or to rely on to efficiently automate analytics operations. The increased number and complexity of analytics-user-interface options pose an obstacle for beginning (and even experienced) analysts to apply and (in some cases) require a rigid input syntax with which inputs must comply. This decreased usability prevents firms and organizations from scaling up analytics operations and from making analytics systems accessible to a broader workforce. In addition to this decreased usability, some of the existing analytics user interfaces hinder firms and other organizations from automating complex analytics operations quickly or, for some operations, from automating the operations all together. The complex and various inputs required for some analytics operations prevent computerized analytics systems from automating such operations and slow down the analytics processing.